Research Article
Social-Cognitive Correlates of Fruit and Vegetable
Consumption in Minority and Non-Minority Youth
Debra L. Franko, PhD1; Tara M. Cousineau, PhD2; Rachel F. Rodgers, PhD1,3;
James P. Roehrig, MA1; Jessica A. Hoffman, PhD1
ABSTRACT
Objective: Inadequate fruit and vegetable (FV) consumption signals a need for identifying predictors and
correlates of intake, particularly in diverse adolescents.
Design: Participants completed an on-line assessment in early 2010.
Setting: Computer classrooms in 4 high schools.
Participants: One hundred twenty-two Caucasian and 125 minority (African American and Hispanic)
high school students (mean age ¼ 15.3 years, SD ¼ 1.0) with parental consent. Response rate was 89%.
Variables Measured: Self-efficacy as measured by confidence in goal setting and decision making about
healthful eating; perceived benefits and barriers to eating FVs; healthful eating-related social support; body
esteem; and FV intake.
Analysis: t tests were used to examine group differences, and binary logistic regression analyses were
conducted to explore the predictors of 5-A-Day FV consumption.
Results: Thirty-four percent of the non-minority group and 28% of the minority group reported eating
5 or more portions of FVs a day (P ¼ .34). Self-efficacy and perceived benefits predicted consumption in
minority participants, whereas barriers and social support were significant predictors in the non-minority
group.
Conclusions and Implications: These findings suggest different variables predict consumption for
minority and non-minority groups and that self-efficacy is an important variable to consider in dietary
change programs for minority adolescents.
Key Words: fruit, vegetable, self-efficacy, minority, youth, adolescence, ethnicity (J Nutr Educ Behav.
2013;45:96-101.)
INTRODUCTION
The positive benefits of adequate fruit
and vegetable (FV) intake are well
documented in studies of health,
obesity, and weight management.1 Research has shown that higher intake of
FVs decreases the risk for chronic diseases and can be beneficial for weight
management.2,3 However, studies
have also indicated that adolescents
rarely eat the recommended number
of FVs each day.4,5 Developing health
promotion interventions to increase
FVs may be particularly challenging
1
with
adolescents,
because
the
negative health consequences of poor
eating habits may not occur until
later in life and are often devoid of
personal immediacy in this age group.
The higher rates of obesity in minority youth are mirrored in their
lower rates of FV consumption.6 Recent studies have continued to note
low rates of FV intake in minority
youth,7,8 as indicated in a report that
showed that 66.4% of Mexican
Americans and 71.9% of African
Americans (ages 2-18 years) did not
meet the recommendations for fruit
Department of Counseling and Applied Educational Psychology, Northeastern University,
Boston, MA
2
BodiMojo, Inc., Cambridge Innovation Center, Cambridge, MA
3
Center for Research and Study in Psychopathology, Toulouse University, Toulouse,
France
Address for correspondence: Debra L. Franko, PhD, Department of Counseling and
Applied Educational Psychology, Northeastern University, 404 International Village,
Boston, MA 02115; Phone: (617) 373-5454; Fax: (617) 373-8892; E-mail: [email protected]
Ó2013 SOCIETY FOR NUTRITION EDUCATION AND BEHAVIOR
http://dx.doi.org/10.1016/j.jneb.2011.11.006
96
consumption, with even higher rates
of inadequate vegetable consumption
(82.9% and 86.1%, respectively).9
Two large-scale survey studies have
found that the low rates of FV
consumption did not statistically
differ between African American and
Hispanic youth.9,10 Although some
have examined predictors of FV
consumption in ethnic minority
children and younger adolescents,11
very few studies have investigated
relevant predictor variables in older
adolescents.12 Targeting older adolescents is important, as lifelong eating
habits begin to consolidate during
this developmental period.13
Low rates of FV consumption have
led the Centers for Disease Control
and Prevention to recommend strategies that are grounded in social cognitive theory to promote greater FV
consumption.14,15 A key construct
emphasized in social cognitive theory
is reciprocal determinism, which
refers to the 2-way interaction between individual and environmental
Journal of Nutrition Education and Behavior Volume 45, Number 2, 2013
Journal of Nutrition Education and Behavior Volume 45, Number 2, 2013
factors to influence behavior. With
regard to FV consumptive behavior,
social cognitive factors include: individual factors (eg, self-efficacy and
body image); interpersonal influences
(eg, family and peers who provide
modeling and expectations); and the
physical environment (eg, benefits
and barriers to consumption). An
important research aim is to identify
tenets of the social-cognitive model
that increase the probability of FV consumption,16-23 particularly in older
and racially and ethnically diverse
adolescents.
This study adds to the existing literature by: (1) examining individual,
interpersonal, and environment factors as predictors of FV consumption;
(2) adding body image as an
individual-level factor that has not
been previously investigated; and (3)
investigating these questions in an
older and racially diverse sample.
The authors expected that higher
self-efficacy and more positive body
image, greater familial and peer support, and lower barriers would predict
higher rates of FV intake in both minority and non-minority adolescents.
METHODS
Participants and Recruitment
Students at 4 urban and suburban Boston public high schools were recruited
for participation in the study. High
schools were chosen by contacting
6 public high schools in the greater
Boston area and inquiring about their
potential interest in the study. Of the
6 schools contacted, 4 agreed to
participate. In these schools, 279 students were given parental consent
forms and information packets in
health class; 247 signed consent forms
were returned, yielding a participation
rate of 89%. The mean age of the sample was 15.31 years (SD ¼ 1.03 y); 141
girls and 106 boys participated, and
minority representation was 49%.
Additional demographic information
is provided in Table 1.
Table 1. Characteristics of Participants from 4 High Schools
n
Age, y
(mean [SD])
Race
Boys Girls Boys
Girls
Caucasian
63
59 15.1 (1.0) 15.1 (1.0)
African American
18
43 15.4 (.85) 15.3 (.81)
Hispanic
16
21 15.9 (1.5) 15.0 (.62)
Biracial (Hispanic
9
18 15.0 (1.3) 15.7 (1.2)
and African American)
physical activity on a 5-point Likerttype scale ranging from ‘‘not sure at
all’’ to ‘‘completely sure.’’24 Only the
items related to healthful eating were
used for the current study. The measure has 2 subscales: healthful eating
goal setting (eg, ‘‘How sure are you
that you can set goals for yourself to
eat healthful food like fruit and vegetables?’’) and healthful eating decision making (eg, ‘‘How sure are you
that you can choose healthful food
to eat every day?’’). Perry et al reported
good convergent, criterion, and discriminant validity for this measure,
which was validated in a diverse sample that included 68.9% Caucasian
(non-Hispanic), 11.3% African American, 3.8% Latino, and 4.7% Asian
American adolescents. Cronbach a in
the current sample was .84 for goal
setting and .90 for decision making.24
The Body Esteem Scale for Adolescents and Adults is a 23-item scale
that taps the affective evaluations of
adolescents' bodies, using a Likert
scale response format from 1 (never)
to 5 (always), with higher scores indicating more positive body esteem.25 A
factor analysis of the scale resulted in
3 subscales: Appearance, Weight, and
Attribution.25 The authors reported
good validity and reliability of this
scale with adolescents,25 and this
measure has been used in studies
with large samples of ethnic minority
adolescents.26 Cronbach a coefficients
for the current sample were .90
(Appearance), .91 (Weight), and .76
(Attribution).
Measures
Individual-level variables. The Physical Activity and Healthy Eating Food
Efficacy Scale for Children is a 20item measure that assesses perceived
confidence to set goals and make decisions about healthful eating and
Franko et al 97
Interpersonal-level variable. The Social Support for Dietary Changes measure assesses psychological and social
support received by family (3 items)
and peers (3 items) when making
Body Mass Index
(mean [SD])
Boys
21.5 (3.1)
22.1 (3.2)
24.1 (4.5)
25.3 (6.8)
Girls
20.9 (1.8)
23.9 (5.7)
20.9 (2.8)
24.8 (5.8)
dietary changes and is scored from 1
(no help) to 5 (a great deal of help).27
Respondents are prompted with
phrases such as, ‘‘If you decided to
try and make changes to your diet so
that it became ‘healthier,’ how much
help would you get from your family
(friends) in making such changes?’’
and ‘‘Would your family (friends) encourage you to keep trying to make
changes if the going got tough?’’ Steptoe and colleagues reported Cronbach
a coefficients of .84 for the family social support scale and .78 for the peer
scale in a study with adults.27 The
Cronbach a for the current sample
was .82 for the family subscale and
.80 for the peer subscale.
Environmental-level variables. Fruit
and Vegetable Benefits and Barriers is
a 12-item validated questionnaire
that measures both perceived benefits
and perceived barriers of eating FVs.27
Examples of specific questions for
benefits are: ‘‘Eating more fruit and
vegetables makes me feel good’’ and
‘‘Eating more fruit and vegetables
will improve the way I look.’’ Specific
questions for barriers are: ‘‘I do not
like fruit and vegetables’’ and ‘‘When
I am with friends, eating fruits and
vegetables can be embarrassing.’’ Steptoe and colleagues reported Cronbach
a coefficients of .72 for benefits and
.78 for barriers in a study of adults.27
However, the authors of the present
study are not aware of this measure
being used with adolescents. In the
current sample, Cronbach a coefficients were .70 and .76, respectively,
for perceived benefits and barriers.
Outcome variable. Participants responded to 2 single-item questions
measuring the number of servings of
98 Franko et al
FVs consumed per day (‘‘How many
servings of fruit/how many servings
of vegetables (not potatoes) do you
usually eat each day?’’). Each question
was followed by examples of what was
meant by a serving. For fruit, the wording was, ‘‘A portion of fruit is an apple
or banana, a small bowl of grapes, or 3
tablespoons of canned fruit.’’ For vegetables, the wording was, ‘‘A serving or
portion of vegetables means 3 heaped
tablespoons of green or root vegetables
such as carrots or parsnips; spinach;
small vegetables like peas, baked
beans, or sweet corn; or a medium
bowl of salad (lettuce, tomatoes,
etc.).’’ Our preliminary pilot-testing
indicated that the wording of this
question was well understood by adolescents.28 This measure has been validated by Steptoe and colleagues
against biochemical markers and
used in a number of studies examining
dietary change in adults.29-31 A recent
study used a similar question with
12- to 14-year-old adolescents to ascertain FV consumption and found good
reliability.22
Procedures
Four public high schools in the Boston metropolitan area participated in
this study. Students in required health
classes were given parental consent
and child assent forms to take home
and return to the teacher within a 2week period. All students with signed
consent forms were given an anonymous identification number and completed the assessment at an individual
station in their school's computer lab
during 1 class period. All assessments
were completed on the Internet at a secure Web site using Concentus software
(version 2, Concentus Assessment
Solutions, Inc., Whittier, CA, 2007).
Participants were weighed, and height
was measured individually in a private
space outside the classroom, with
shoes removed. Systematic anthropometric techniques followed those
described by Lohman et al.32 Weight
was measured to 0.1 kg on a digital
electronic scale (Seca, Creative Health
Products, Plymouth, MI, 2009).
Standing height was measured to 0.1
cm with a portable stadiometer (Shorr
Productions, Olney, MD, 2007). The
study was reviewed and approved by
the Office of Human Subjects Protection at Northeastern University.
Journal of Nutrition Education and Behavior Volume 45, Number 2, 2013
Data Analysis
In order to examine differences associated with ethnic minority status, the
sample was dichotomized into those
who self-identified as Caucasian and
a second group who self-identified as
African-American, Hispanic, or biracial. As the data were determined to
be normally distributed, t tests were
used to examine group differences on
study variables. Binary logistic regression analyses were conducted to explore the predictors of 5-A-Day FV
consumption within each group, controlling for sex. A post hoc power analysis was conducted for the logistic
regression using G*Power 2 (HeinrichHeine-University, Dusseldorf, Germany, 2007).33 Odds ratios for the
predictors of FV consumption in the 2
groups were compared using the procedure outlined by Altman and Bland.34
In this analysis, a general test for interaction, comparing both estimates with
their standard error, was used to test the
strength of the differences in predictor
variables between the minority and
non-minority groups. All analyses
were conducted using PASW Statistics
(version 18.0, SPSS Inc., Chicago, IL,
2009).
RESULTS
Descriptive Statistics
Summary statistics according to ethnic status are presented in Table 2.
Significant differences were found between the groups on both subscales of
the self-efficacy measure, healthful
eating goal setting (P < .05), and
healthful eating decision making
(P < .01).
Characteristics Associated with
5-A-Day FV Consumption
Each group was classified according to
whether or not the group members
met the recommended guidelines of
5 daily portions of FVs (5-A-Day
group). Thirty-four percent of the
Caucasian group (n ¼ 42) and 28%
(n ¼ 35) of the minority group met
these guidelines. A chi-square test indicated that the difference in rates between the groups was not significant
(c2 [1, 247] ¼ 0.27, P ¼ .34).
Within each ethnic group, participants who met the 5-A-Day guidelines
were compared to those who ate fewer
FVs on the dependent measures
(Table 3). Significant differences were
found within the Caucasian group,
with the 5-A-Day participants reporting lower scores on the measure of
perceived barriers to FV consumption
(P < .05). Though the differences did
not meet significance, this group also
reported high levels of peer support
9.5 (2.89) > 8.4 (3.23), healthful eating goal setting 7.6 (1.75) > 6.9
(1.98), healthful decision making
27.6 (6.93) > 25.0 (7.81), and appearance satisfaction 2.8 (0.73) > 2.5
(0.76). Among the ethnic minority
group, the 5-A-Day group reported
significantly higher levels of healthful
eating goal setting (P < .001), higher
Table 2. Summary Statistics for Study Variables by Ethnic Status, mean (SD)
Age, y
Healthful eating decision making
Healthful eating goal setting
FV barriers
FV benefits
Appearance satisfaction
Weight satisfaction
Appearance attribution
Peer social support
Family social support
Caucasian
Group (n ¼ 122)
15.2 (1.01)
25.9 (7.6)
7.2 (1.92)
11.2 (3.97)
21.6 (3.99)
2.6 (0.76)
2.6 (0.92)
2.3 (0.75)
8.8 (3.18)
11.5 (2.67)
Ethnic Minority
Group (n ¼ 125)
15.4 (1.04)
23.1 (6.61)**
6.5 (2.02)*
12.0 (4.25)
21.2 (4.19)
2.7 (0.91)
2.4 (1.02)
2.4 (0.89)
8.9 (3.18)
10.9 (3.44)
FV indicates fruit and vegetable.
*P < .05; **P < .01.
Note: Asterisks indicate significant mean differences (t tests) between the
Caucasian group and the ethnic minority group.
Journal of Nutrition Education and Behavior Volume 45, Number 2, 2013
Franko et al 99
Table 3. Differences between Participants Meeting or Not Meeting the 5-A-Day Guidelines by Ethnic Status, mean (SD)
Caucasian Group
Healthful eating decision making
Healthful eating goal setting
FV barriers
FV benefits
Appearance satisfaction
Weight satisfaction
Appearance attribution
Peer social support
Family social support
5-A-Day (n ¼ 42)
27.6 (6.93)
7.6 (1.75)
9.9 (3.48)
21.9 (5.34)
2.8 (0.73)
2.7 (0.95)
2.4 (0.75)
9.5 (2.89)
11.2 (2.58)
Less than 5 (n ¼ 80)
25.0 (7.81)
6.9 (1.98)
11.8 (4.08)*
21.5 (3.10)
2.5 (0.76)
2.5 (0.90)
2.2 (0.75)
8.4 (3.23)
11.6 (2.73)
Ethnic Minority Group
5-A-Day (n ¼ 35)
24.7 (7.43)
7.5 (1.75)
10.1 (3.39)
23.4 (4.17)
2.6 (1.03)
2.4 (1.10)
2.2 (0.96)
9.2 (3.62)
11.8 (3.54)
Less than 5 (n ¼ 90)
22.4 (6.24)
6.1 (1.98)**
12.8 (4.32)**
20.3 (3.90)**
2.7 (0.86)
2.4 (1.00)
2.5 (0.85)
8.8 (3.00)
10.6 (3.37)
FV indicates fruit and vegetable.
*P < .05; **P < .001.
Note: Asterisks indicate significant mean differences (t tests) between participants who met the 5-A-Day guidelines and those
who ate fewer fruit and vegetables within each ethnic group.
perceived benefits of FV consumption
(P < .001), and lower levels of barriers
to FV consumption (P < .001). Although the difference did not meet
significance, this group also reported
high levels of family social support
11.8 (3.54) > 10.6 (3.37).
Predicting 5-A-Day FV
Consumption
To explore the predictors of 5-A-Day
FV consumption, logistic regressions
were conducted, controlling for sex,
using healthful eating decision making and goal setting, perceived barriers
and perceived benefits to FV consumption, and family/peer social support as predictors (Table 4). Among
the Caucasian participants, a test of
the full model against a constantonly model was statistically significant (c2 ¼ 14.27, P < .05, df ¼ 6,
Nagelkerke R2 ¼ 0.15). Prediction
success overall was 67%. Perceived
barriers to FV eating (P < .05) and family social support (P < .05) were significant negative predictors, whereas
peer social support (P < .05) was a significant positive predictor. Sex was
not found to be a significant predictor
in the regression analysis.
Among the ethnic minority group,
a test of the full model against a constant-only model was statistically significant (c2 ¼ 24.19, P < .001, df ¼
6, Nagelkerke R2 ¼ 0.26). The overall
percentage correctly classified was
74%. Healthful eating goal setting (P
< .05) and perceived benefits of FV
consumption (P < .05) were positive
predictors. Sex was not significantly
related to FV consumption in this
group. However, note that power
was limited for these analyses (post
hoc power analysis, power ¼ 0.30).
The analysis was rerun, controlling
for both sex and body mass index,
and the results were very similar,
except that the family social support
variable was no longer significant
among the Caucasian group. As seen
in Table 4, a comparison of the odds
ratios between the 2 groups indicated
that the differences were significant (P
< .05 to .001, depending on the variable tested).
DISCUSSION
Targeting interventions to improve
the dietary behaviors of adolescents
is a priority and represents a unique
challenge. Using social cognitive theory to better understand the correlates
of adolescent health behaviors among
diverse demographic groups may inform program planning and public
health campaigns. In this study,
individual, interpersonal, and environmental variables were found to
predict FV consumption in line with
the authors' expectations based on
the social cognitive framework. Selfefficacy levels were associated with
higher FV consumption in both
groups, although overall levels of
self-efficacy, as measured both by confidence in goal setting and decision
making about healthful eating, were
found to be higher in non-minority
participants. These data suggest that
the minority sample experienced less
confidence in their ability to eat in
healthful ways, relative to the nonminority sample. Although a number
of studies have emphasized the importance of self-efficacy for healthful eating behaviors in adult samples19,35,36
and primarily non-minority adolescent samples,37-39 to the authors'
knowledge, few have examined selfefficacy in minority samples.12 The
present data suggest that self-efficacy
is a particularly important target for dietary interventions and that future research might investigate ways to
promote greater self-efficacy in health
promotion programs for diverse
groups of adolescents.
For both minority and nonminority youth, greater barriers were
associated with less FV intake, a link reported by others,23,39 which suggests
that decreasing barriers to healthful
eating may be an important route to
improving eating behaviors. The scale
used in this study examined a variety
of barriers toward eating FVs, some of
which were interpersonally focused
(‘‘When I am with friends, eating fruit
and vegetables can be embarrassing’’;
‘‘My family does not like fruit and
vegetables’’), whereas others were
more practically focused (‘‘Fruits and
vegetables are inconvenient to eat’’).
However, this scale did not specifically
examine the multitude of barriers that
may make FV consumption more
difficult, such as availability and
Journal of Nutrition Education and Behavior Volume 45, Number 2, 2013
100 Franko et al
Table 4. Results from the Logistic Regression Analyses Predicting Consumption of 5 Daily Portions of FVs
Caucasian Group
ß
c2
P
Step 1
Sex
.25 0.41 NS
Step 2
Healthful eating decision making
.00 0.03 NS
Healthful eating goal setting***
.03 0.03 NS
FV barriers**
.16 4.70 < .05
FV benefits*
.06 0.98 NS
Peer social support*
.16 3.82 < .05
Family social support*
.18 4.13 < .05
OR
95% CI
Ethnic Minority Group
R2
0.05
ß
c2
.03 0.00
1.28 0.60-2.70
P
NS
OR
95% CI
.97 0.43-2.22
0.15
1.00
1.03
.85
.94
1.18
.83
0.93-1.09
0.74-1.4
0.74-.96
0.84-1.06
1.00-1.4a
0.70-.99
R2
0.00
0.26
.03
.31
.11
.14
.10
.09
0.88 NS
.96
4.23 < .05 1.36
2.34 NS
.90
5.36 < .05 1.16
1.45 NS
.90
1.16 NS 1.09
0.89-1.04
1.02-1.82
0.78-1.03
1.02-1.31
0.76-1.06
0.93-1.29
CI indicates confidence interval; FV, fruit and vegetable; NS, not significant; OR, odds ratio.
*P < .05; **P < .005; ***P < .001; aThe non-rounded CI for this predictor was significant [1.001, 1.393].
Note: Asterisks indicate significant differences in ORs between the Caucasian group and the ethnic minority group as
described by Altman and Bland.34
accessibility. Efforts such as those of
Rolnick et al that emphasize a close
examination of the barriers to
healthful eating will be important
building
blocks
in
program
development.40
Within the minority sample, perceived benefits and barriers, as well as
self-efficacy in relation to goal setting,
separated those who ate 5-A-Day from
those who did not. Previous studies
have indicated greater barriers to FV
availability in minority samples,41
and this study confirms those results
in a sample of adolescents. The finding
that goal setting (as a component of
self-efficacy) was a robust correlate of
consumption in minority youth has
implications for incorporating this
teachable skill when devising FV promotion programs and has not been reported in earlier studies. A unique
contribution of this finding, then, is
that including goal setting when devising programs for minority youth may
be an important avenue toward increasing FV consumption in this group.
As in previous studies,20,38,42 both
family and peer support were
associated with FV intake, although
only significantly so in the nonminority sample. It is not clear why
social support did not predict intake
in the minority sample; however,
low statistical power may have been
an issue in these analyses, as indicated
by the power analysis results. Efforts
to increase peer involvement, specifically, in the encouragement of FV
consumption are worthwhile, as it is
likely that leveraging support and social accountability may be key factors
in promoting health behavior change
in this group.43
Limitations of this study include
the single location of schools, the selfreported nature of FV intake, and the
relatively small sample size, particularly for the group who reported higher
FV consumption. Low power for the
predictor analyses indicates these findings should be viewed with caution.
Overall, this study highlights the
role of self-efficacy in FV consumption for both minority and nonminority youth. Benefits and barriers
to healthful eating were associated
with intake in both minority and
non-minority adolescents. The results
of this study have implications for interventions designed to promote
healthful eating in youth and suggest
that these social-cognitive variables
should be included as targets for
change, particularly with diverse samples of adolescents.
ACKNOWLEDGMENTS
This study was funded through the
National Institutes of Health Grant
#2R44DK074280-03. We are most appreciative to the teachers, students,
and principals at Arlington High
School, Framingham High School,
Milton High School, and the Edward
M. Kennedy Academy for Health
Careers for their participation in this
study. We also thank our consultants,
Carolyn Butterworth, RD, RN, MS,
Carmen Sceppa, MD, PhD, Kirsten
Davidson, PhD, Theresa Nicklas,
DrPH, Carol Torgan, PhD, Hillary
Wright, RD, and Deborah Rohm
Young, PhD, who made significant
contributions to the project.
REFERENCES
1. Diamant AL, Babey SH, Wolstein J,
Jones M. Obesity and diabetes: two
growing epidemics in California. Policy
Brief UCLA Cent Health Policy Res.
2010;(PB2010–7):1-12.
2. Oude Griep LM, Geleijnse JM,
Kromhout
D,
Ocke
MC,
Verschuren WM. Raw and processed
fruit and vegetable consumption and
10-year coronary heart disease incidence
in a population-based cohort study in the
Netherlands. PLoS One. 2010;5:e13609.
3. Rolls BJ, Ello-Martin JA, Tohill BC.
What can intervention studies tell us
about the relationship between fruit
and vegetables consumption and weight
management? Nutr Rev. 2004;62:1-17.
4. Grunbaum JA, Kann L, Kinchen S,
et al. Youth risk behavior surveillance—United States, 2003. MMWR
Surveill Summ. 2004;53:1-96.
5. Guenther Pm, Dodd KW, Reedy J,
Krebs-Smith SM. Most Americans eat
much less than recommended amounts
of fruits and vegetables. J Am Diet
Assoc. 2006;106:1371-1379.
6. Robinson T. Applying the socioecological model to improving fruit
and vegetable intake among lowincome African Americans. J Community Health. 2008;33:395-406.
Journal of Nutrition Education and Behavior Volume 45, Number 2, 2013
7. Eaton DK, Kann L, Kinchen S, et al.
Youth risk behavior surveillance—
United States, 2009. MMWR. Surveill
Summ. 2010;59:1-142.
8. Striegel-Moore R, Thompson D,
Affenito S, et al. Fruit and vegetable intake: few adolescent girls meet national
guidelines. Prev Med. 2006;42:223-228.
9. Lorson BA, Melgar-Quinonez HR,
Taylor CA. Correlates of fruit and vegetable intakes in US children. J Am Diet
Assoc. 2009;109:474-478.
10. Beech BM, Rice R, Myers L, Johnson C,
Nicklas TA. Knowledge, attitudes, and
practices related to fruit and vegetable
consumption of high school students. J
Adolesc Health. 1999;24:244-250.
11. Knai C, Pomerleau J, Lock K,
McKee M. Getting children to eat
more fruit and vegetables: a systematic
review. Prev Med. 2006;42:85-95.
12. Bruening M, Kubik MY, Kenyon D,
Davey C, Story M. Perceived barriers
mediate the association between selfefficacy and fruit and vegetable consumption among students attending
alternative high schools. J Am Diet Assoc.
2010;110:1542-1546.
13. Nelson MC, Story M, Larson N, Neumark-Sztainer D, Lytle LA. Emerging
adulthood and college-aged youth: an
overlooked age for weight-related
behavior change. Obesity. 2008;16:
2205-2211.
14. Centers for Disease Control and Prevention. Guidelines for school health
programs to promote lifelong healthy
eating. MMWR Morb Mortal Wkly
Rep. 1996;45:1-41.
15. Bandura A. Health promotion from the
perspective of social cognitive theory.
Psychol Health. 1998;13:623-649.
16. Brug J. Determinants of healthy eating:
motivation, abilities and environmental
opportunities. Fam Pract. 2008;25(suppl
1):i50-i55.
17. Sheeshka
JD,
Woolcott
DM,
MacKinnon NJ. Social cognitive theory
as a framework to explain intentions to
practice healthy eating behaviors. J
Appl Soc Psychol. 1993;23:1547-1573.
18. Bandura A. Self-efficacy: The Exercise of
Control. New York, NY: WH Freeman/
Times Books/Henry Holt & Co; 1997.
19. Anderson ES, Winett RA, Wojcik JR.
Self-regulation, self-efficacy, outcome
expectations, and social support: social
cognitive theory and nutrition behavior. Ann Behav Med. 2007;34:304-312.
20. Pearson N, Ball K, Crawford D. Predictors of changes in adolescents’ consumption of fruits, vegetables and
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
energy-dense snacks. Br J Nutr. 2010;
105:795-803.
Cutler GJ, Flood A, Hannan P, Neumark-Sztainer D. Major patterns of dietary intake in adolescents and their
stability over time. J Nutr. 2009;139:
323-328.
Pearson N, Atkin AJ, Biddle SJ,
Gorely T. A family-based intervention
to increase fruit and vegetable consumption in adolescents: a pilot study.
Public Health Nutr. 2010;13:876-885.
Yeh M, Ickes S, Lowenstein L, et al.
Understanding barriers and facilitators
of fruit and vegetable consumption
among a diverse multi-ethnic population in the USA. Health Promot Int.
2008;23:42-51.
Perry CM, De Ayala RJ, Lebow R,
Hayden E. A validation and reliability
study of the Physical Activity and
Healthy Food Efficacy Scale for Children (PAHFE). Health Educ Behav.
2008;35:346-360.
Mendelson B, Mendelson M, White D.
Body-esteem scale for adolescents and
adults. J Pers Assess. 2001;76:90-106.
Mackey ER, La Greca AM. Does this
make me look fat? Peer crowd and
peer contributions to adolescent girls’
weight control behaviors. J Youth Adolesc. 2008;37:1097-1110.
Steptoe A, Perkins-Porras L, Rink E,
Rink E, Hilton S, Cappuccio FP. Psychological and social predictors of changes in
fruit and vegetable consumption over
12 months following behavioral and
nutrition education counseling. Health
Psychol. 2004;23:574-581.
Cousineau TM, Franko DL, Trant M,
et al. Teaching adolescents about changing bodies: randomized controlled trial
of an Internet puberty education and
body dissatisfaction prevention program.
Body Image. 2010;7:296-300.
Cappuccio FP, Rink E, PerkinsPorras L, McKay C, Hilton S,
Steptoe A. Estimation of fruit and vegetable intake using a two-item dietary
questionnaire: a potential tool for primary health care workers. Nutr Metab
Cardiovasc Dis. 2003;13:12-19.
Perkins-Porras L, Cappuccio FP,
Rink E, Hilton S, McKay C,
Steptoe A. Does the effect of behavioral
counseling on fruit and vegetable intake
vary with stage of readiness to change?
Prev Med. 2005;40:314-320.
Steptoe A, Perkins-Porras L, McKay C,
Rink E, Hilton S, Cappuccio FP. Behavioural counselling to increase fruit
and vegetable consumption in low in-
Franko et al 101
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
come adults: randomised trial. BMJ.
2003;326:855-858.
Lohman TG, Roche AR, Martorell R.
Anthropometric Standardization Reference
Manual. Champaign, IL: Human Kinetics; 1988.
Faul F, Erdfelder E, Buchner A,
Lang A-G. Statistical power analyses
using G*Power 3.1: tests for correlation
and regression analyses. Behav Res
Methods. 2009;41:1149-1160.
Altman DG, Bland JM. Interaction revisited: the difference between two estimates. BMJ. 2003;326:219.
Strachan S, Brawley L. Healthy-eater
identity and self-efficacy predict
healthy eating behavior: a prospective
view. J Health Psychol. 2009;14:684-695.
Watters JL, Satia JA, Galanko JA. Associations of psychosocial factors with
fruit and vegetable intake among African-Americans. Public Health Nutr.
2007;10:701-711.
Ball K, MacFarlane A, Crawford D,
Savige
G,
Andrianopoulos
N,
Worsley A. Can social cognitive theory
constructs explain socio-economic variations in adolescent eating behaviors?
A mediation analysis. Health Educ Res.
2009;24:496-506.
Kalavana TV, Maes S, De Gucht V. Interpersonal and self-regulation determinants of healthy and unhealthy eating
behavior in adolescents. J Health Psychol. 2010;15:44-52.
Lytle LA, Varnell S, Murray DM, et al.
Predicting adolescents’ intake of fruits
and vegetables. J Nutr Educ Behav.
2003;35:170-175.
Rolnick SJ, Calvi J, Heimendinger J, et al.
Focus groups inform a web-based program to increase fruit and vegetable intake. Patient Educ Couns. 2009;77:314-318.
Cullen KW, Thompson DI, Scott AR,
Lara-Smalling A, Watson KB,
Konzelmann K. The impact of goal attainment on behavioral and mediating
variables among low income women
participating in an Expanded Food and
Nutrition Education Program intervention study. Appetite. 2010;55:305-310.
Zabinski M, Daly T, Norman GJ, et al.
Psychosocial correlates of fruit, vegetable, and dietary fat intake among adolescent boys and girls. J Am Diet Assoc.
2006;106:814-821.
Hoffman JA, Thompson DR,
Franko DL, Power TJ, Leff SS,
Stallings VA. Decaying behavioral effects in a randomized, multi-year fruit
and vegetable intake intervention.
Prev Med. 2011;52:370-375.